Cloud Service Provider Selection Research Articles

Differentially private and truthful auction-based resource procurement for budget-constrained DAG applications in clouds

With the rapid proliferation of emerging scientific computing, more and more cloud users (CUs) with large-scale applications are seeking efficient executing paradigms in clouds. Following this trend, a cloud market with multiple cloud service providers (CSPs) is emerging, which aims to provide efficient cross-cloud services to CUs through the cooperation among CSPs. In the market, CUs can procure different virtual machine resources and their combinations from one or more CSP(s) in a pay-as-you-go way. In such a procurement process, it is challenging to achieve privacy protection and truthfulness of CSPs while enabling flexible resource combinations. As such, we design a differentially private and truthful auction-based resource procurement mechanism (DTARP) in clouds. Specifically, we first propose a budget-aware resource profile determination algorithm (BRPD) to obtain a set of resource profiles, in order to bridge the difference between application tasks and resources under the budget constraint. Next, we design a differential privacy-based winning CSP selection algorithm to choose a set of winning CSPs, and the payment calculation algorithm to calculate the payment for each winning CSP. Strict theoretical analysis proves that the proposed DTARP achieves differential privacy, truthfulness, individual rationality, computational efficiency, and approximate social cost minimization while guaranteeing the efficient completion of application tasks under the budget. Extensive simulation results also demonstrate the effectiveness of the proposed DTARP.

Development and Implementation of a Cognitive Cloud Assistant for Optimal Cloud Service Provider Selection

Abstract This paper explores the design and implementation of a cognitive assistant powered by cloud computing. We begin by reviewing existing cloud service providers and assistant solutions. Building on this foundation, we define a set of requirements for our cognitive assistant and utilize them to guide the development process. To validate the functionality of our creation, we compare its outputs with established reference architectures.

Developing sustainability, resiliency, agility, and security criteria for cloud service providers’ viability assessment: A comprehensive hierarchical structure

Cloud Service Provider (CSP) selection based on several functional attributes, such as transparency, price, latency, CPU performance, availability, and restorability, has taken much more attention in the Industry 4.0 era. On the other hand, after the COVID-19 pandemic, viability has been noticed as the crucial specification of supply chains to overcome mega crises. This paper proposes a comprehensive hierarchical structure for the viable cloud service provider assessment problem according to the reviewed literature on CSPA, viable supplier selection, and suppliers’ resiliency, sustainability, agility, and security. The proposed framework involves seven criteria, 29 sub-criteria, 181 indices, and the corresponding descriptions.

Proportional intuitionistic fuzzy CODAS method: Cloud service provider selection application

Intuitionistic fuzzy sets aims at taking the hesitancy of an expert into account in assigning a membership degree or a non-membership degree. The direct assignment of decimal numbers for membership and non-membership degrees of an element in intuitionistic fuzzy sets is not practical. Besides, the assigned degrees are generally composed of one digit or at most two digits after dot. This problem has not been addressed as much as it deserves in the literature. The hypothesis of the paper is that the determination of proportional relationships between membership and non-membership degrees is more appropriate than the direct assignment to obtain the degrees. Proportional intuitionistic fuzzy (PIF) sets require only the proportion relations between an intuitionistic fuzzy set’s parameters. The accuracy of the results obtained with multi-criteria decision-making models definitely depends on how accurately the membership degrees are determined. In this paper, we extend Combinative distance-based assessment (CODAS) method by using single-valued proportional intuitionistic fuzzy sets. We compare the proposed PIF CODAS method with ordinary fuzzy CODAS method. A cloud service provider selection problem is handled to show the validity of the proposed PIF CODAS method. Additionally, a comparative analysis and a sensitivity analysis together with a discussion are presented.

A Comparative Study of K-Medoids and Fuzzy K-Means Clustering for the Selection of Optimal Cloud Service Provider

The wide range of services offered by cloud service providers, including data processing, storage and computing resources, makes it difficult to choose the best provider for a particular application. A workable approach is provided by clustering algorithms, which group cloud service providers according to common characteristics including price, reliability, security, and performance. Among all clustering techniques, fuzzy K-means and K-medoids stand out as popular choices. Non-parametric K-means finds cluster representatives called Medoids, while fuzzy K-means soft clustering method places data points into several clusters with different membership levels. Using these algorithms makes it easy to compare and select the cloud service provider best suited for a particular application. The current research examines the application of K-medoids and fuzzy K-means clustering techniques and its performance matrix such as Silhouette Score, Cluster Cohesion, Cluster Separation, Calinski-Harabasz Index(CH), Davies-Bouldin Index(DBI), Inertia and Adjusted Rand Index (ARI) in cloud service provider selection.

Cross-entropy method for multiple attribute decision making under the triangular fuzzy neutrosophic sets

Cloud computing is emerging as an advanced stage of enterprise information technology in a highly competitive society, and is now in a phase of rapid development. Enterprises using cloud computing can reduce the cost of building infrastructure and reap huge benefits. Due to the convenience of cloud computing, more and more enterprises are inclined to use cloud services to build their business systems. However, there are many different cloud service providers in the market, and enterprises need scientific decision-making tools to determine which cloud service provider to choose. The cloud service provider selection is viewed as the multi-attribute decision-making (MADM). In this paper, the cross-entropy method under triangular fuzzy neutrosophic sets (TFNSs) is built based on the traditional cross-entropy method. Firstly, the TFNSs is introduced. Then, combine the traditional fuzzy cross-entropy method with TFNSs information, the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) method is established for MADM under TFNSs. Finally, a numerical example for cloud service provider selection has been given and some comparisons is used to illustrate advantages of cross-entropy method with TFNSs.

Optimal cloud service provider selection: An MADM framework on correlation-based TOPSIS with interval-valued q-rung orthopair fuzzy soft set

The interaction between two factors significantly measures the results' exactness. But, acquiring information for statistical computation is frequent, and the data obtained could be challenging to interpret. To predict how a particular factor will fluctuate regarding another as well, the correlation coefficient (CC) is usually employed. But this approach is rarely utilized for interval-valued q-rung orthopair fuzzy soft set (IVq-ROFSS). The situation in which IVq-ROFSS grows as it is modern, along with a broad depiction of the q-rung orthopair fuzzy soft set (q-ROFSS), engaging for a more reflective and precise assessment. The current study explores the CC and weighted correlation coefficient (WCC) for IVq-ROFSS and their fundamental characteristics. This research is designed to improve the prioritization technique for order preference by similarity to the ideal solution (TOPSIS) with expanded measures. Also, to check the linearity of the intended approach, we integrated mathematical formulations of correlation constrictions. This study demonstrates that the suggested methodology is a robust multi-attribute decision-making (MADM) tool for intricate information set interpretation and prioritizing. We presented a numerical illustration demonstrating the actual application of our recommended decision-making strategy for choosing Cloud Service Providers (CSPs) in cloud service management. The approach developed in this research is superior to conventional models in maintaining the precise structure of the determined studies. Thus, the algorithm produces more reliable and consistent decisions. The influence of our studies grows within the scope of this research, as the originated algorithm can enhance the ability to analyze realities and make informed decisions in light of the information provided. Therefore, this study can significantly impact data analysis and decision-making by revealing the importance of the proposed TOPSIS approach and the vitality of perpetual development in methods for making decisions to get more reliable and precise outcomes.

Strategies for efficient resource management in federated cloud environments supporting Infrastructure as a Service (IaaS)

The increased market activity and visibility have resulted in a growing demand for additional computer resources to meet the needs of cloud clients. Delivering promised quality of service (QoS) while fulfilling diverse resource demands dynamically might be difficult for a single Cloud Service Provider (CSP). Federated cloud computing, also known as distributed cloud computing, is an established paradigm in which CSPs collaborate to aggregate unused resources, resulting in financial and QoS benefits. Notably, this strategy improves availability and reliability while overcoming individual CSP difficulties in preserving QoS amid fluctuations in resource demand. The federated cloud successfully leverages computing resources even during low-demand periods, demanding a comprehensive resource management strategy for Infrastructure as a Service (IaaS) inside participating CSPs. Such a strategy is crucial for preserving QoS, ensuring availability and dependability, and optimizing underutilized computing resources. This research presents the novel IaaS cloud design, revealing a unique methodology that reimagines traditional cloud systems. The proposed IaaS cloud framework investigates virtual machine migration and resource consolidation, building a strong foundation founded on IaaS principles and emphasizing the crucial role of virtualization. The technique introduces ground-breaking concepts such as Cloud User (CU) and Reputation Management, which are strengthened by specific algorithms that improve cloud service security and trust. Furthermore, the combination of Trust Manager (TM) and Broker Manager (BM) components strengthens SLA control and trust evaluation, aligning smoothly with IaaS standards to improve service quality and reliability. User Profiling, which is classified into private, social, and corporate profiles, provides a separate lens for successful cloud user management inside the IaaS landscape, allowing for customised service delivery. SMI and cutting-edge ranking algorithms, such as the Deep Q-based Algorithm, optimize cloud service provider selection and ranking—an important aspect of IaaS. The use of the Banker's algorithm and a comprehensive Service Level Agreement (SLA) Management plan provides efficient resource allocation, mirroring recognized IaaS standards. This research study not only throws light on these trailblazing techniques, but it also establishes a new standard for IaaS cloud design and resource management.

Cloud-Based Software Development Lifecycle: A Simplified Algorithm for Cloud Service Provider Evaluation with Metric Analysis

At present, hundreds of cloud vendors in the global market provide various services based on a customer's requirements. All cloud vendors are not the same in terms of the number of services, infrastructure availability, security strategies, cost per customer, and reputation in the market. Thus, software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental activities. Thus, there is a need to evaluate various cloud service providers (CSPs) and platforms before choosing a suitable vendor. Already existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service attributes. However, they require more time to collect data, simulate and evaluate the vendor. The proposed work compares various CSPs in terms of major metrics, such as establishment, services, infrastructure, tools, pricing models, market share, etc., based on the comparison, parameter ranking, and weightage allocated. Furthermore, the parameters are categorized depending on the priority level. The weighted average is calculated for each CSP, after which the values are sorted in descending order. The experimental results show the unbiased selection of CSPs based on the chosen parameters. The proposed parameter-ranking priority level weightage (PRPLW) algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.

Selection of cloud service providers using MCDM methodology under intuitionistic fuzzy uncertainty

Selection of cloud service providers using MCDM methodology under intuitionistic fuzzy uncertainty

An Enhanced MCDM Model for Cloud Service Provider Selection

Multi-Criteria Decision-Making (MCDM) techniques are often used to aid decision-makers in selecting the best alternative among several options. However, these systems have issues, including the Rank Reversal Problem (RRP) and decision-making ambiguity. This study aimed to propose a selection model for a Cloud Service Provider (CSP) that addresses these issues. This research used the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the alternatives. The entropy technique is utilized to determine the weight of the criteria, and Single Valued Neutrosophic (SVN) is employed to address uncertainty. To select the best cloud provider based on Quality of Service (QoS) criteria, we used a dataset from Cloud Harmony for this study. The results indicated that the suggested model could effectively resolve the RRP under conditions of uncertainty. This research is novel and is the first to address both the problem of uncertainty in decision-making and RRP in MCDM.

A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

Cloud computing technology has undergone tremendous growth in recent years, and there are now many cloud service providers (CSPs). This makes CSP selection a challenging process for cloud users. Further complications arise when users modify the priorities of their requirements. Moreover, concerns such as complex computation, inconsistencies, and rank reversal have been raised in current approaches, resulting in less reliable results. This study presents a new hybrid multiple-criteria decision-making (MCDM) framework for ranking cloud services based on Markov chains combined with the best-only method (BOM). The Markov chain is used to record and track the changes in the priorities of user requirements and determine their final values. Then, the BOM method is utilized to determine the final weights of the QoS criteria based on pairwise comparisons made by the cloud user or decision maker and the final priorities of the user’s requirements. Finally, the cloud services are ranked, and the best CSP is selected. The proposed framework was validated using a case study and a real dataset. Performance, consistency, rank conformance, and sensitivity analyses were performed to evaluate the proposed framework. The obtained results prove that the proposed framework is computationally efficient and fully consistent, considers the user requirements and its transition pattern, and is robust to rank reversal.

An Integrated Multi-Criteria Decision-Making Model for Cloud Service Provider Selection

In the present scenario, it is inevitable for organizations to use the services offered through the cloud. As there are numerous cloud service providers, the users have a vast number of choices to get the desired service. For the long term success of any organization, it is important to choose the right cloud service provider. This paper focuses on proposing a new framework to select the best cloud service provider. While selecting the best service provider, using an appropriate method for finding out the weight of each criterion is very important as it directly impacts the ranking of candidate service providers. In this work, the Full Consistency Method (FUCOM) is employed for finding the weight of each criterion and Multi-Objective Optimization by Ratio Analysis (MOORA) is proposed for ranking the service provider. The integration of FUCOM with MOORA results in computing the reliable values of criteria weight and a simpler procedure for computing the overall score of the service provider. Hence a rational decision making is possible in selecting the best cloud service provider.

Cloud-CoCoSo: Cloud Model-Based Combined Compromised Solution Model for Trusted Cloud Service Provider Selection

Cloud-CoCoSo: Cloud Model-Based Combined Compromised Solution Model for Trusted Cloud Service Provider Selection

Application of ROC and CODAS Techniques for Cloud Service Provider Selection

Due to the widespread use of cloud services, the choice of cloud service providers has become an important problem for companies. It is a strategic decision-making problem for companies to determine the most suitable service provider that can meet their expectations and needs. Choosing the most effective service provider for a firm depends on numerous criteria based on the firm's strategies, needs and resources. Therefore, in the present study, an integrated decision model based on Rank Order Centroid (ROC) and Combinative Distance-Based Assessment (CODAS) techniques has been developed. In the first stage of the application, the criteria were evaluated using the ROC method, and the service providers were listed using the CODAS technique in the second stage. The effectiveness of the proposed model has been tested by a software company that wants to select a cloud service provider. The results of the research are expected to contribute to service providers and firms that want to select cloud service providers that can meet their needs.

Performance based Risk driven Trust (PRTrust): On modeling of secured service sharing in peer-to-peer federated cloud

Peer-to-peer (P2P) federated cloud system has emerged as a promising service delivery model wherein multiple cloud service providers (CSPs) collaborate and share resources and services among themselves to fulfill the spikes in demand from their cloud service consumers (CSCs). It facilitates CSPs to accomplish the committed service level agreements (SLAs) to their CSCs. However, the lack of preexisting trust relationships and unawareness about the cloud infrastructure and service delivery performance among CSPs in a distributed environment poses a risk to the quality of service (QoS) being delivered. We address this challenge by proposing a trust model, PRTrust, for a peer-to-peer federated cloud system that capitalizes the triangular relationship of performance, risk, and trust for the participating CSPs. The main contributions of this work are: (1) providing a logical design and architecture for trust and performance management in a peer-to-peer federated cloud system; (2) providing a two-tier weighted performance evaluation mechanism for CSPs; (3) providing a risk evaluation mechanism for CSPs based on their current performance level; (4) providing an improvised mechanism to evaluate and manage personalized reputation-based trust for the participating CSPs; (5) providing a CSP selection mechanism from a trusted list of CSPs, using the evaluated performance-based risk, for sharing the resources and services in a peer-to-peer federated cloud system. The proposed PRTrust model has shown better threat resilient behavior in dealing with malicious peer CSPs when compared with the reference EigenTrust model.

An integrated group decision‐making framework for selecting cloud service providers based on regret theory and EVAMIX with hybrid information

Handing computing assets to cloud service providers (CSPs) to obtain cloud services is one of the important strategies for enterprises to embrace the digital era, and CSP selection is a crucial decision-making process for cloud deployment. However, there are many criteria involved in selecting an optimal CSP, not all of which can be accurately quantified. Therefore, CSP selection is a typical hybrid-information decision-making problem, in which criterion evaluation values are expressed in various forms. Meanwhile, the psychological behavior of the CSP selection team also has a significant impact on the decision-making result, which is poorly considered in the existing research results on CSP selection. Thus, in this paper, a new group decision-making support framework incorporating regret theory is constructed to select CSPs with hybrid information. Initially, various forms of hybrid information are processed separately to avoid the distortion of heterogeneous information caused by traditional conversion methods. Then, considering the psychology of regret aversion, the respective regret–rejoice functions for hybrid information are defined. Subsequently, regret–rejoice values are introduced into the evaluation of mixed data method framework, and a decision-making support procedure based on it is established, in which an expert weight determination method based on the maximizing consensus model is proposed, and the group best–worst method is used to calculate criteria weights. Afterwards, an illustrative example of CSP selection is given to clarify the implementation process of the proposed method. Finally, the effectiveness and superiority of the proposed decision-making framework in selecting CSPs are explained through parameter analysis and comparison with existing methods.

Optimal Cloud Service Provider Selection Based on QoS Metrics

There are thousands of providers obtainable in the market, and more and more are being added to the service list every day. All of these providers claim that the services given to them are distinctive and hassle-free. To check their claim, the cloud service broker (CSB) verifies the quality of service (QoS) of the cloud service providers (CSPs) and the level of user needs. Depending on the requirements of the cloud consumer (CC), CSB allocates a CSP to it. This paper proposed optimal cloud service provider selection (OCSPS) based on QoS metrics. The CC would handovers the demand to the CSB for optimal CSP selection using QoS. Once the CSP selection process is complete, the outcomes would become back to the CC. Entire demands created to CCs are saved in the request buffer (RB) in the CSB. When a specific request is fulfilled, the subsequent demand would take from this RB. The experimental result shows that the proposed OCSPS algorithm takes less time for CSP ranking and optimal CSP selection.

Efficiency Measurement of Cloud Service Providers Using Network Data Envelopment Analysis

An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements <xref ref-type="bibr" rid="ref13" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[13]</xref> . To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, the non-oriented network slacks-based measure (SBM) model and conventional SBM model with the assumptions of constant returns to scale (CRS) and variable returns to scale (VRS) are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.

An MCDM Approach for Cloud Computing Service Selection Based on Best-Only Method

Owing to the significant and continuous development of cloud computing technology and the increasing number of cloud service providers (CSPs) that have emerged, CSP selection has become a challenging decision for many organizations. To accurately evaluate the services provided by various CSPs, many independent criteria must be considered, resulting in a multi-criteria decision-making (MCDM) problem. In MCDM problems, several alternatives are assessed against several criteria to identify the optimal alternative (s). The challenges stem from complex computation as well as less consistency, resulting in less reliable results. In this paper, a new method, called the best-only method (BOM), is proposed for solving the CSP selection problem, which is viable, efficient, and fully consistent. The proposed method was evaluated and validated using a use-case scenario, demonstrating its efficiency and appropriateness. The proposed method is compared with two popular MCDM methods, analytical hierarchical process (AHP) and best-worst method (BWM), from three perspectives: efficiency, consistency ratio (CR), and total deviation (TD). Based on the obtained results, AHP and BWM require 75.3% and 46.2% more comparisons, respectively, than the proposed method. The consistency ratios for AHP, BWM, and the proposed method were 37.92%, 13.35%, and 0%, respectively. The total deviations are 21.26, 8.65, and 0 for AHP, BWM, and the proposed method, respectively. These results clearly indicate that the proposed method outperforms AHP and BWM in terms of computing complexity and consistency, making it more efficient and reliable.